<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 3.2//EN">
<html>
<head>
<title>fMRI Sandbox - the Buttons</title>
</head>
<body bgcolor="#FFFFFF" text="#000000" link="#0000FF" vlink="#800080" alink="#FF0000">
<h2>The buttons section:</h2>
<h2>File :</h2>
<h3><a name="load">Load :</a></h3>
Allows you to either load a single analyze format image (consisting of one *.img and one *.hdr image) or alternatively a 4-D
dataset in x*y*z*t format (e.g. 96x48x40x150), which should be named idat.
<h3><a name="save">Save:</a></h3>
Save the state of your actual work to disk. This button saves the image data in its current state (idat) as well as the
sandbox experiment struct into a file specified with a name you can choose.
<h3><a name="Batch">Batch:</a></h3>
Once you click this button, a predefined sequence of events is called up, which is now part of the main Sandbox program, but
will shortly be transferred into a config file. Use this as a kind of batch function to get things done quicker and in a
standardized way.
<h3><a name="DesignLoad">Load Design :</a></h3>
You have the option here to go for either a
<h4><a href="blocked_design_GUI.html">simple GUI</a></h4>
that allows you to generate a simplified blocked design, or a more
<h4><a href="er_design_GUI.html">complex GUI</a></h4>
that allows you to define or load an event-related design. In case you are from the Logothetis lab, it also allows you to
load a .dgz file that was generated by the QNX state system that we use here to synchronize our experimental stimuli with the
scanner environment. Feel free to write additonal import routines to add support for your own experiments.
<h3><a name="SPMinteract">SPM interaction</a></h3>
You have a couple of options here to integrate SPM with fMRI Sandbox
<h2>Trial :</h2>
<h3><a name="All">All:</a></h3>
Select only those scans that were part of a trial. This is helpful if you are using motion detection and control methods in
animal experiments
<h3><a name="None">None:</a></h3>
Unselect all trials. You might want to do this when you have selected a number of trials, but would rather like to start all
over again
<h3><a name="artrem">Remove Artefacts:</a></h3>
Use the slider to select the artefacts to be removed. Check manually if the artefacts labelled correspond to your visual
impression and adjust the slider to maximize overlap with your own observation, so that you are not labelling volumes that
you would not consider to be defective. Labelled volumes are indicated with a white +.Once you are satisfied, push the button
to remove the volumes and corresponding experiment datapoints from your study. Take note that you cannot undo the procedure
without reloading the original image. You could also simply <a href="#save">save</a> the experiment now, retaining an index
of all defective volumes that you could later use e.g. in SPM
<h3><a name="LabelSelected">Label selected</a></h3>
Use this button, if you don't want to remove the selected volumes, but just label them as defective for further processing.
Trials containing artefacts are going to be removed from the intrial index, such that they are not used for further
processing.
<h3><a name="keep">Keep selected:</a></h3>
Keep all selected trials, delete the remaining volumes. This gives you (in an ideal world) just the volumes that were part of
a trial and are therefore (again, in an ideal world) motion-corrected.
<h3><a name="deltrial">Delete Selected</a></h3>
Delete all selected trials, keep the remaining volumes. Use this function to remove trials that you have detected as faulty,
if you do not want to delete single volumes e.g. in a case where the deletion of baseline volumes would lead to a distorted
normalization
<h3><a name="link">Delete volume:</a></h3>
Use this button to just remove a specific volume from the experiment, e.g. if you have detected an artefact that the
automatic detection algorithm could not find.
<h3><a name="DelVolume">Delete Volume</a></h3>
Deletes the selected volume. Use this to get rid of clearly artifactual volumes the the detection algorithm did not find.
<h2>Brain:</h2>
<h3><a name="movement">Movement</a></h3>
Realignment is helpful in experiments in which the actual subject head movement is very limited but the impression arises
that movement takes place. This might be due to field inhomogeneities and might be corrected with this realignment algorithm.
This is especially useful for awake animal experiments in high-field scanners, where there should not be motion in any other
direction.
<h4><a name="MovementData">Movement data</a></h4>
Use this selection field to display the amount of in-phase motion of the brain, and select various realignment procedures for
realignment in y-Direction.
<h4><a name="Rea3DCOM">Realignment 3D-COM</a></h4>
3D- Center of Mass is usually quite reliable, but your mileage may vary, please feel free to try the other options.
<h4><a name="ReaCorr">Realignment n-D Corr</a></h4>
Correlation tries to minimize the correlation between volumes
<h4><a name="Rea2DCorr">2-D CoM</a></h4>
2D Center of Mass uses the mass center of single slices for realignment, and is thus more susceptible to outliers.
<h4><a name="ReaDFT">DFT</a></h4>
Discrete Fourier Transform tries to match images in Fourier-Space and is usually also quite stable.
<h3><a name="corint">Correct intensity:</a></h3>
In case your image has an intensity gradient, it might be helpful to homogenize the image intensity prior to brain extraction
to improve results. Your mileage may vary.
<h3><a name="lensfilt">Apply Lens Filter</a></h3>
Use a lens filter, if your image contains a lot of tissue around the brain that the brain extraction algorithm cannot get rid
of. This reduces the image intensity outside of the brain and enhances the image intensity inside the brain. Use the slider
to determine the strength of the filtering. Position the crosshair in the middle of the part of the image you want to
enhance.
<h3><a name="extrbrain">Extract:</a></h3>
This serves to strip a brain of surrounding tissue. The slider determines the extent of the tissue removal. Once you are set,
press the button to do the actual extraction and keep the extracted image. If you cannot get satisfactory results, try using
the <a href="#lensfilt">lens filter</a> or <a href="#corint">correcting image intensity</a> .
<h3><a name="crop">Crop</a></h3>
This removes all the empty space around the brain, thereby reducing the image size in memory and on disk. This greatly speeds
up later processing steps, but generates new images that might not be compatible with other images from a different scanning
session
<h3><a name="padding">Pad</a></h3>
Padding takes images that have been shrunk with bounding and pads them with empty space on all sides to make concatenation
between scans possible. It takes the smallest one of a series of different volume sizes that can be changed in the scripts by
the user.
<h3>Process:</h3>
<h4><a name="RemoveOutliers">Remove outliers</a></h4>
Use this selection to remove outliers from your data. An outlier is defined as a datapoint more than 3 standard deviations
below or above the mean of every voxel time course. Outliers will be set to 3 SD above/below the voxel time course mean.
Sandbox converts all image input data into int16 format. In the course of the process, values that happen to be greater than
32767 are truncated and set to 32767. This function removes values that are far above the image mean or below zero from the
image data. This is sometimes helpful if the normalization procedure induced strange values, but your mileage may vary. Use
with caution.
<h4><a name="NormalizeVolumes">Normalize Volumes:</a></h4>
Use this function to normalize the average image intensity of every scan to the global mean of all scans. This reduces
variance due to field or shimming changes during a scanning session or between sessions, but does decrease the signal of
interest if the perfusion in large parts of the brain changes due to the stimulus.
<h4><a name="DetrendScans">Detrend Scans:</a></h4>
Use this function to remove a linear trend from the scans.
<h4><a name="DetrendScansNonlinear">Detrend Scans Nonlinear:</a></h4>
Use this function to remove a nonlinear trend from the scans.
<h4><a name="InterpolateFilter">Interpolate Artefacts and Filter scans:</a></h4>
Interpolate artefact periods in concatenated data and filter data. Input and output to this function are concatenated
<h4><a name="InterpolateFilterRunByRun">Interpolate Artefacts parallel run by run:</a></h4>
Interpolate artefact periods in unconcatenated, complete datasets. Algorithm optimized to temporally inand filter data with
an optimized algorithm. Input and output to this function are not concatenated
<h4><a name="EnhanceContrast">Enhance Contrast:</a></h4>
Enhance contrast between white and grey matter in image data. This might sometimes help with coregistration issues.
<h4><a name="ScanNorm">Normalize Scans:</a></h4>
Due to shim and field changes, different scans may have a different baseline value for every voxel. This function calculates
the mean value of every voxel during every scan, and normalizes this to the mean of all scans that are to be analyzed.
<h4><a name="TrialNorm">Normalize Trials:</a></h4>
Due to motion-induced field changes within a scan, different trials may have a different baseline value for every voxel. This
function calculates the mean value of every voxel during every trial, and normalizes this to the mean of all scans that are
to be analyzed. The drawback is that it will also reduce the difference that there may exist between different trials.
<h4><a name="BaseNormMult">Normalize Baseline Multiplicative:</a></h4>
Calculates the mean value of every baseline voxel during every trial, derives a muliplication factor for each baseline with
which the average of the trial baseline would be equal to the mean of all trial baselines and normalizes the trials
accordingly. It assumes that the first regressor is modelled to give you a baseline estimate. Avoid this if you don't have a
baseline regressor or if your baseline is very short (e.g. 1 volume) or contains artefacts, as this would lead to a weird
distortion of the temporal course of the data.
<h4><a name="BaseNormDiv">Normalize Baseline Divisive:</a></h4>
Divides each trial by the mean of the baseline value, thereby removing baseline variance from the data. Avoid this if you
don't have a baseline regressor or if your baseline is very short (e.g. 1 volume) or contains artefacts, as this would lead
to a weird distortion of the temporal course of the data. Note that applying this function will remove structural information
from your data, e.g. you will not see a "brain" any more, as just the variance due to the trial is kept.
<h4><a name="BaseNormAdd">Normalize Baseline Additive:</a></h4>
Calculates the mean value of every trial baseline voxel during every trial, and derives an absolute value that would need to
be added or subtracted from the trial baseline mean to fit the baseline average of every trial. This value is then used to
normalize the trials accordingly. It assumes that the first regressor is modelled to give you a baseline estimate. Avoid this
if you don't have a baseline regressor or if your baseline is very short (e.g. 1 volume) or contains artefacts, as this would
lead to a weird distortion of the temporal course of the data.
<h4><a name="BaseZ-score">Normalize Baseline Z-score:</a></h4>
Every trial is divided by its mean baseline intensity
<h4><a name="NormalizeNoise">Normalize Noise:</a></h4>
Calculates the average noise time course and uses this to normalize the data. You might want to try this if you want to
remove variance visible in the noise from your data
<h4><a name="NormalizeZScore">Normalize Z-score:</a></h4>
Does a Z-score transform over all data. Data transformation is done by subtracting the mean voxel value from the individual
voxel value and then dividing the difference by the standard deviation
<h3><a name="SNRThreshold">SNR Threshold:</a></h3>
Use the slider to determine a SNR value that you want to set as a threshold to remove noise from your data.<br>
Use the button to remove the noise.
<h3><a name="ShowSlices">Show slices:</a></h3>
If this box is checked, you will see all slices of the currently selected volume displayed in a different figure.
<h3><a name="AnatomyOverlay">Anatomy overlay:</a></h3>
If this box is checked, you will be prompted to load an anatomical image which will then be overlaid on the actual functional
information. Use this to display functional maps on anatomical images, but note that the overlap is very far from perfect,
and no normalization or field-map correction is done at this point.
<h3><a name="tsmooth">Temporal Smooth:</a></h3>
Temporally smooothing the image reduces some of the noise that occurs during functional imaging and thereby improves
statistical results somewhat.<br>
Use the slider to determine the value, use the button to start the smoothing process
<h3><a name="ssmooth">Spatial Smooth:</a></h3>
Spatially smooth the image with a smoothing filter determined by the slider value. This is most useful for improving the
results of functional imaging data. A value of 2 voxels gives good results in general. It uses an optimized routine from the
<a href="http://chronux.org/">chronux project</a> . Depending on the size of your dataset, it may take up to several minutes,
though, be warned!<br>
Use the slider to determine the value, use the button to start the smoothing process
<h2>Map:</h2>
<h3><a name="calmap">Calculate Map</a></h3>
Calculate a map for the selected statistical method. Be warned, this can take several minutes depending on the selected
method (Correlation is fastest, GLM is slowest).
<h3><a name="pred">Predictor:</a></h3>
Choose one of the predictors you have predefined and loaded into memory.
<h3><a name="model">Model:</a></h3>
Chose the statistical model that best fits your hypotheses. Correlation is fastest for a quick preview of what you get from
applying different preprocessing steps. GLM is the standard algorithm established by SPM but the version here does not
correct for multiple comparisons. Robust regression uses a different method that is less susceptible to outliers, but still
does not correct for multiple comparisons.
<h3>Map Threshold:</h3>
Use the <a name="corrslid">slider</a> to find a correlation and probability threshold that the maps that you see overlaid on
the images are just random noise. Negative correlation values are overlaid in blue on the images, positive correlation values
in red.
<h3><a name="clusthr">Cluster threshold</a></h3>
Use this <a name="clusslid">slider</a> to determine of how many voxels clusters should consist at least in order to be
displayed.
<h3><a name="dispmap">Map display on</a></h3>
Displays a statistical map overlaid on the brain, if you have loaded at least one hemodynamic predictor. This is calculated
on the fly just for the displayed sections. The calculation omits voxels with an intensity value that is smaller than a third
of the average brain intensity, thereby reducing the calculation time. Typically, the calculation takes about a second, more
for larger datasets. Once a section has been displayed, the values are stored, speeding up the calculation if other sections
sharing the same voxels are viewed.
<h3><a name="SelecTrialdisp">Only selected</a></h3>
Displays the map (and time course) for the selected trial only.
<h3><a name="TrialTimecourse">Trial Timecourse</a></h3>
Displays the timecourses of all trials and their mean overlaid on it to the right of the button.
<h3><a name="ClusterData">Cluster data</a></h3>
Switches between ROI and cluster timecourse display.
<h3><a name="ROI">ROI size</a></h3>
Use this <a name="roislid">slider</a> to determine how big the green ROI should be.
<h2><a name="Explore">Explore:</a></h2>
<h3><a name="ShiftHrf">Reset HRF shift:</a></h3>
Use the slider to shift the predictor timecourse by fractions of one volume forward or back and calculate the model you have
selected. This might come in handy if there is some weird kind of offset between stimulus and scanner. Use the button to
reset the shift to zero.
<h3><a name="SmoothHRF">Smooth HRF:</a></h3>
Smoothes your HRF predictor in cases where there might be some jitter that is unaccounted for. I'm not sure if this helps,
though...
<h3><a name="AddBase">AddBase/RemBase</a></h3>
Adds one more baseline volume to your trials or removes one baseline from the baseline of your trials.
<h3><a name="Fine-Tune">Fine-Tune</a></h3>
This function shifts the experiment predictors relative to your imaging session in order to maximize the correlation between
the current predictor chosen and the mean value of the ROI. This is useful if you have reason to suspect that there might be
an offset issue, e.g. if you cannot see anything in visual cortex with a strong visual stimulus. Use with caution!
<h2>General:</h2>
<h3>Help:</h3>
well, somehow you made it here, didn't you?
<h3>Undo:</h3>
Sometimes, the Undo button even works! Incredible, isn't it?
<h3><a name="col">Color:</a></h3>
Select different colormaps for the images. This is sometimes useful to inspect your data for artefacts.
<h3>Write Mean Brain</h3>
Writes the mean of all volumes to disk as an analyze image.
<h3>Write Selected</h3>
Writes just the currently selected volume to disk as analyze image
<h3>FlipVol</h3>
Flips the volumes in the selected dimension. I included this because I could not find it anywhere else...
<h2>Information:</h2>
Here you see the current header information. Should you wish to change the voxel size, enter the new values here.
</body>
</html>